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Examining the bidirectional associations between adiposity and cognitive function using population-level data

2022· dissertation· en· W6981758941 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2022
Typedissertation
Langueen
DomaineSocial Sciences
ThématiquePrivacy, Security, and Data Protection
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCognitionAssociation (psychology)Stroop effectLongitudinal studyMediationEffects of sleep deprivation on cognitive performanceFunction (biology)Cognitive flexibility
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: The association between adiposity and cognitive function has been extensively explored in previous literature, and numerous cross-sectional and longitudinal analyses suggest a reliable association. However, most previous studies on this topic were predominantly executed with a narrow, unidirectional assumption that baseline adiposity predicts future cognitive function (i.e., the “brain-as-outcome” perspective). Literature within neuropsychology, the cognitive neurosciences and cognitive epidemiology suggests that baseline cognitive function may also predict the development of adiposity (i.e., the “brain-as-predictor” perspective), although this reverse directionality has not been extensively explored to date using population-level datasets. Instead, relatively small-scale experimental studies have shown that temporary attenuation of some facets of cognitive function, particularly the executive control domain, could result in disinhibited eating. Therefore, it is plausible that impaired cognitive function affects the implementation of behaviors that confer downstream risk for adiposity. Taken together, these findings suggest that the association between adiposity and cognitive function could be reciprocal, but bidirectional effects have not been explored systematically in previous literature. This dissertation aimed to examine the hypothesized bidirectional associations between adiposity and cognitive function and their possible mediation paths using population-level datasets in three age groups: older adults, middle-aged adults, and adolescents.
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\nMethods: Studies 1 and 2 were conducted using the Canadian Longitudinal Study on Aging (CLSA) datasets. Study 1 was a cross-sectional analysis of the baseline CLSA comprehensive cohort (N = 30,097), whereas Study 2 was a prospective analysis of the baseline and first follow-up datasets. The bidirectionality hypotheses were examined using three indicators of cognitive function (animal fluency, Stroop interference, and mean reaction time) and four indicators of adiposity (body mass index [BMI], total fat mass, waist circumference [WC] and waist-hip ratio [WHR]). Hierarchical multivariable regression, multivariate multivariable regression and cross-lagged panel model with latent variable modeling (CLPM-L) were employed to test the study hypotheses. Mediation analyses were conducted for lifestyle (e.g., diet, physical activity) and physical health status (e.g., hypertension, blood pressure and diabetes) variables. 
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\nStudy 3 was a prospective analysis of the Adolescent Brain Cognitive Development (ABCD) dataset (N = 11,878). The above-mentioned bidirectional hypotheses were examined using two indicators of adiposity (e.g., BMI z score [zBMI] and WC) and five indicators of cognitive function included in the NIH Toolbox Cognitive Battery (e.g., Flanker, pattern recognition, picture sequence, picture vocabulary and oral reading tasks). Multivariate multivariable regression and CLPM-L were employed to test the study hypotheses. Mediation analyses were conducted for lifestyle (e.g., diet, physical activity) variables, physical health status (e.g., blood pressure) variables, and lateral prefrontal cortex (PFC) morphology features (volume and thickness).
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\nResults: Study 1 showed that measures of cognitive functions were significantly associated with adiposity after controlling for confounders in cross-sectional analysis of the CLSA baseline datasets. In general, superior performance on animal fluency, Stroop, and reaction time tasks was associated with lower adiposity by most metrics. These associations were more substantial for moderate- and high-income sub-populations and mediated through lifestyle behavior (e.g., diet and physical activity) and physical health conditions (e.g., diabetes and diet). 
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\nStudy 2 suggested that higher baseline adiposity was associated with higher Stroop interference at follow-up for both middle-aged and older adults. Similarly, higher baseline Stroop interference was associated with higher follow-up adiposity, but only in middle-aged adults. Effects involving semantic fluency and processing speed were less consistent. The above effects persisted following covariate adjustments and when used latent variable modeling of the adiposity variable. Significant mediation effects were observed for blood pressure, diabetes, and diet.
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\nStudy 3 revealed that higher baseline zBMI and WC were associated with worse follow-up picture sequence and better picture vocabulary task performance, respectively. Likewise, superior baseline performance on Flanker and picture sequence tasks was associated with better follow-up adiposity status. A bidirectional association was observed between episodic memory and zBMI. Latent adiposity modeling showed a bidirectional association with executive function (measured by Flanker task) but not with other cognitive domains. Significant mediation effects were observed for blood pressure, physical activity, and lateral PFC volume/thickness. 
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\nConclusion: This dissertation examined the possibility of bidirectional associations between adiposity and cognitive function among older adults, middle-aged adults, and adolescents. Findings suggested that bidirectional associations between adiposity and cognitive function exist among adolescents and middle-aged individuals. In contrast, findings involving older adult population supported primarily a “brain-as-outcome” perspective on the association between adiposity and cognitive function.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,420
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,098
Tête enseignante GPT0,297
Écart entre enseignants0,198 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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